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      Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands

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          Abstract

          Background

          Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA).

          Results

          MLDA was programmed in R (version 2.7.0) and the package is available at CRAN [ 1]. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing.

          Conclusion

          MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.

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          Most cited references25

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            CpG islands in vertebrate genomes.

            Although vertebrate DNA is generally depleted in the dinucleotide CpG, it has recently been shown that some vertebrate genes contain CpG islands, regions of DNA with a high G+C content and a high frequency of CpG dinucleotides relative to the bulk genome. In this study, a large number of sequences of vertebrate genes were screened for the presence of CpG islands. Each CpG island was then analysed in terms of length, nucleotide composition, frequency of CpG dinucleotides, and location relative to the transcription unit of the associated gene. CpG islands were associated with the 5' ends of all housekeeping genes and many tissue-specific genes, and with the 3' ends of some tissue-specific genes. A few genes contained both 5' and 3' CpG islands, separated by several thousand base-pairs of CpG-depleted DNA. The 5' CpG islands extended through 5'-flanking DNA, exons and introns, whereas most of the 3' CpG islands appeared to be associated with exons. CpG islands were generally found in the same position relative to the transcription unit of equivalent genes in different species, with some notable exceptions. The locations of G/C boxes, composed of the sequence GGGCGG or its reverse complement CCGCCC, were investigated relative to the location of CpG islands. G/C boxes were found to be rare in CpG-depleted DNA and plentiful in CpG islands, where they occurred in 3' CpG islands, as well as in 5' CpG islands associated with tissue-specific and housekeeping genes. G/C boxes were located both upstream and downstream from the transcription start site of genes with 5' CpG islands. Thus, G/C boxes appeared to be a feature of CpG islands in general, rather than a feature of the promoter region of housekeeping genes. Two theories for the maintenance of a high frequency of CpG dinucleotides in CpG islands were tested: that CpG islands in methylated genomes are maintained, despite a tendency for 5mCpG to mutate by deamination to TpG+CpA, by the structural stability of a high G+C content alone, and that CpG islands associated with exons result from some selective importance of the arginine codon CGX. Neither of these theories could account for the distribution of CpG dinucleotides in the sequences analysed. Possible functions of CpG islands in transcriptional and post-transcriptional regulation of gene expression were discussed, and were related to theories for the maintenance of CpG islands as "methylation-free zones" in germline DNA.
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              Number of CpG islands and genes in human and mouse.

              Estimation of gene number in mammals is difficult due to the high proportion of noncoding DNA within the nucleus. In this study, we provide a direct measurement of the number of genes in human and mouse. We have taken advantage of the fact that many mammalian genes are associated with CpG islands whose distinctive properties allow their physical separation from bulk DNA. Our results suggest that there are approximately 45,000 CpG islands per haploid genome in humans and 37,000 in the mouse. Sequence comparison confirms that about 20% of the human CpG islands are absent from the homologous mouse genes. Analysis of a selection of genes suggests that both human and mouse are losing CpG islands over evolutionary time due to de novo methylation in the germ line followed by CpG loss through mutation. This process appears to be more rapid in rodents. Combining the number of CpG islands with the proportion of island-associated genes, we estimate that the total number of genes per haploid genome is approximately 80,000 in both organisms.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2008
                8 August 2008
                : 9
                : 337
                Affiliations
                [1 ]Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK
                [2 ]Centre for Integrative Cancer Biology, Ohio State University, Columbus, USA
                [3 ]Translational Medicine Research Centre, University of Dundee, UK
                [4 ]The Beatson West of Scotland Cancer Centre, Cancer Research UK Clinical Trial Unit, Glasgow, UK
                Article
                1471-2105-9-337
                10.1186/1471-2105-9-337
                2529322
                18691414
                d699464d-7f20-476e-927b-b1361e675827
                Copyright © 2008 Dai et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 April 2008
                : 8 August 2008
                Categories
                Methodology Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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